Python Machine Learning Bootcamp

Python Machine Learning Bootcamp: Course Description

This step-by-step course will start with regressions, one of the most common building blocks of machine learning. Then you’ll learn about different ways to approach machine learning like k-nearest neighbors, decision trees, and random forest.

Learn key statistical concepts including bias, various, and overfitting, and how to measure the accuracy of your models. Statistics is the foundation of many topics in machine learning and you'll learn to apply those concepts to machine learning. 

Use Python's sci-kit learn library to do a variety of machine learning algorithms. Learn different types of machine learning algorithms and when you should use them. Create your own models, test them, and evaluate the performance with hands-on projects. 

Learn to solve real-world problems by working on real projects in this hands-on course and be ready to take your new skills into the real world.

Prerequisites: This course does require students to be comfortable with Python and its data science libraries (NumPy and Pandas)

Instructor Bio

Rob joins our team after years of experience in the FinTech industry, where he worked on data analytics tools for banks and credit unions. Most recently, he was a senior developer on a targeted marketing tool that won an industry-wide innovation award and is now used by over 80 institutions. Rob holds a B.A. in Mathematics from Wesleyan University and an M.Sc. in Mathematical Logic from the Universiteit van Amsterdam.

This course is delivered in partnership with Noble Desktop, a leading design and coding school in NYC with over three decades of experience.